Using stochastic computation graphs formalism for optimization of sequence-to-sequence model. - Eugene A. Golikov; Vlad Zhukov; Maksim Kretov (CoRR, 2017)

Embedding-reparameterization procedure for manifold-valued latent variables in generative models. - Eugene A. Golikov; Maksim Kretov (CoRR, 2018)

An Essay on Optimization Mystery of Deep Learning. - Eugene A. Golikov (CoRR, 2019)

Quadratic number of nodes is sufficient to learn a dataset via gradient descent. - Biswarup Das; Eugene A. Golikov (CoRR, 2019)

Towards a General Theory of Infinite-Width Limits of Neural Classifiers. - Eugene A. Golikov (ICML, 2020)

Dynamically Stable Infinite-Width Limits of Neural Classifiers. - Eugene A. Golikov (CoRR, 2020)

Notes on Deep Learning Theory. - Eugene A. Golikov (CoRR, 2020)

Non-Gaussian Tensor Programs. - Eugene A. Golikov; Greg Yang (NeurIPS, 2022)

Feature Learning in $L_2$-regularized DNNs: Attraction/Repulsion and Sparsity. - Arthur Jacot; Eugene A. Golikov; Clément Hongler; Franck Gabriel (NeurIPS, 2022)

Feature Learning in L2-regularized DNNs: Attraction/Repulsion and Sparsity. - Arthur Jacot; Eugene A. Golikov; Clément Hongler; Franck Gabriel (CoRR, 2022)

Neural Tangent Kernel: A Survey. - Eugene A. Golikov; Eduard Pokonechny; Vladimir Korviakov (CoRR, 2022)

A review of neural architecture search. - Dilyara Baymurzina; Eugene A. Golikov; Mikhail Burtsev 001 (Neurocomputing, 2022)

A Generalization Bound for Nearly-Linear Networks. - Eugene A. Golikov (Trans. Mach. Learn. Res., 2025)